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Related Experiment Video

Updated: Jan 26, 2026

New Methods to Study Gustatory Coding

New Methods to Study Gustatory Coding

Published on: June 29, 2017

FACSCaps: Pose-Independent Facial Action Coding with Capsules.

Itir Onal Ertugrul¹, Lászlό A Jeni¹, Jeffrey F Cohn^1,2

¹Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops

|April 5, 2019

Summary

This summary is machine-generated.

FACSCaps, a novel architecture, enhances facial expression analysis by effectively detecting facial action units (AUs) across various head poses. This method improves accuracy in real-world conditions where head rotation is common.

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Area of Science:

Computer Vision
Machine Learning
Affective Computing

Background:

Current automated facial expression analysis methods struggle with non-frontal head poses, limiting real-world applicability.
Existing multi-view Convolutional Neural Networks (CNNs) increase model size and may generalize poorly to unseen views.

Purpose of the Study:

To introduce FACSCaps, a single model for multi-view, multi-label facial action unit (AU) detection.
To develop a system that generalizes to novel head poses and provides insights into model learning through face synthesis.

Main Methods:

FACSCaps utilizes matrix capsules to model video frames, incorporating hierarchical pose relationships.
The model is trained via joint optimization of multi-label loss and reconstruction accuracy.
Evaluation employed the FERA 2017 dataset, featuring spontaneous expressions with diverse head orientations.

Main Results:

FACSCaps demonstrated superior performance in facial action unit detection across a wide range of head orientations.
The proposed architecture outperformed state-of-the-art CNNs and their temporal extensions on the FERA 2017 dataset.

Conclusions:

FACSCaps offers a robust solution for facial expression analysis under varying head poses.
The matrix capsule approach provides a more generalized and effective method for multi-view facial action unit detection.